optimizer_rmsprop  R Documentation 
Optimizer that implements the RMSprop algorithm
optimizer_rmsprop(
learning_rate = 0.001,
rho = 0.9,
momentum = 0,
epsilon = 1e07,
centered = FALSE,
weight_decay = NULL,
clipnorm = NULL,
clipvalue = NULL,
global_clipnorm = NULL,
use_ema = FALSE,
ema_momentum = 0.99,
ema_overwrite_frequency = 100L,
jit_compile = TRUE,
name = "RMSprop",
...
)
learning_rate 
Initial value for the learning rate:
either a floating point value,
or a 
rho 
float, defaults to 0.9. Discounting factor for the old gradients. 
momentum 
float, defaults to 0.0. If not 0.0., the optimizer tracks the
momentum value, with a decay rate equals to 
epsilon 
A small constant for numerical stability. This epsilon is "epsilon hat" in the Kingma and Ba paper (in the formula just before Section 2.1), not the epsilon in Algorithm 1 of the paper. Defaults to 1e7. 
centered 
Boolean. If 
weight_decay 
Float, defaults to NULL. If set, weight decay is applied. 
clipnorm 
Float. If set, the gradient of each weight is individually clipped so that its norm is no higher than this value. 
clipvalue 
Float. If set, the gradient of each weight is clipped to be no higher than this value. 
global_clipnorm 
Float. If set, the gradient of all weights is clipped so that their global norm is no higher than this value. 
use_ema 
Boolean, defaults to FALSE. If TRUE, exponential moving average (EMA) is applied. EMA consists of computing an exponential moving average of the weights of the model (as the weight values change after each training batch), and periodically overwriting the weights with their moving average. 
ema_momentum 
Float, defaults to 0.99. Only used if 
ema_overwrite_frequency 
Int or NULL, defaults to NULL. Only used if

jit_compile 
Boolean, defaults to TRUE. If TRUE, the optimizer will use XLA # noqa: E501 compilation. If no GPU device is found, this flag will be ignored. 
name 
String. The name to use for momentum accumulator weights created by the optimizer. 
... 
Used for backward and forward compatibility 
The gist of RMSprop is to:
Maintain a moving (discounted) average of the square of gradients
Divide the gradient by the root of this average
This implementation of RMSprop uses plain momentum, not Nesterov momentum.
The centered version additionally maintains a moving average of the gradients, and uses that average to estimate the variance.
Optimizer for use with compile.keras.engine.training.Model
.
Other optimizers:
optimizer_adadelta()
,
optimizer_adagrad()
,
optimizer_adam()
,
optimizer_adamax()
,
optimizer_ftrl()
,
optimizer_nadam()
,
optimizer_sgd()
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